东亚大气可吸入颗粒物时空分布的数值模拟研究

利用嵌套网格空气质量模式(Nested Air Quality Prediction Model System,NAQPMS)模拟研究了2010年东亚地区可吸入颗粒物(PM10)的时空演变,并初步评估了其对人群健康的危险度.结果表明,NAQPMS模式能够合理地反映东亚地区PM10的时空分布,不同季节观测值和模拟值的总体相关系数达到0.65~0.85.2010年东亚PM10的地面浓度高值区(100μg·m-3)出现在我国华北、华中和内蒙古中西部等地区.其中,无机盐(硫酸盐、硝酸盐和铵盐)对我国东部PM10的贡献最大(10~70μg·m-3,20%~50%);一次PM10次之(5~50μg·m-3,10%~30%),有机物(5~30μg·m-3,10%~20%)和黑炭(3~20μg·m-3,3%~5%)紧随其后.PM10可以引起我国东部人群急性总死亡率增加2%~5%,对我国居民的健康水平构成了一定威胁.     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen(T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks(ANNs) and support vector machines(SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination(R~2), Nash–Sutcliff efficiency(NSE), relative efficiency criteria(d rel). Additionally, Latin-Hypercube one-factor-at-a-time(LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.     

Predicting vehicle fuel consumption patterns using floating vehicle data

The status of energy consumption and air pollution in China is serious. It is important to analyze and predict the different fuel consumption of various types of vehicles under different influence factors. In order to fully describe the relationship between fuel consumption and the impact factors, massive amounts of floating vehicle data were used. The fuel consumption pattern and congestion pattern based on large samples of historical floating vehicle data were explored, drivers'' information and vehicles'' parameters from different group classification were probed, and the average velocity and average fuel consumption in the temporal dimension and spatial dimension were analyzed respectively. The fuel consumption forecasting model was established by using a Back Propagation Neural Network. Part of the sample set was used to train the forecasting model and the remaining part of the sample set was used as input to the forecasting model.     

Assessing and testing prediction uncertainty for single tree-based models: A case study applied to northern hardwood stands in southern Québec, Canada

Estimating prediction uncertainty for a single tree-based model is hindered by the complex structure of these models. In this paper, we addressed this issue with a case study applied to northern hardwood stands in Québec, Canada. SaMARE is a stochastic single tree-based model that was designed for these types of stands. Using a Monte Carlo approach, the model can provide a mean predicted value and its confidence limits for some plot-level attributes.The mean predicted values were compared to observed values in terms of bias and accuracy. In addition to these common statistics, we compared nominal coverage of Monte Carlo-simulated confidence intervals with real (observed) coverage to verify the adequacy of the simulated uncertainty. A comparison was made using several plot-level attributes, which exhibited an increasing discriminative complexity. This complexity ranges from coarse attributes, such as all-species basal area, up to more complex ones, such as basal area for stems of a particular species and with sawlog potential.The results showed that in terms of absolute value, biases were small, but could be relatively high with respect to the average observed value when the discriminative complexity of the attribute increased. The comparison between nominal and real coverage of confidence intervals gave satisfactory results for all-species plot-level attributes. However, for some species-specific attributes, the Monte Carlo-simulated confidence intervals overestimated the real coverage.     

高速公路交通噪声预测模型探讨

针对我国当前广泛使用的2种高速公路噪声预测模型《06规范》预测模型与《09导则》预测模型在预测时比较研究,重点利用环境现状监测数据分别对2种模型验证与对比分析.结果表明,2种模型预测值与实测值相差3dB ～5dB,车流量＞ 300辆/h,《09导则》更接近实测值；在夜间车流量＜300辆/h,《06规范》更接近实测值,2种模型结合采用《06规范》计算的车速,距离衰减考虑车流量的大小,在此基础上应用《09导则》,预测结果与实测值更为接近.     

上海市城郊臭氧浓度变化分析及模拟预报

利用上海市普陀、南汇、淀山湖3个臭氧观测站2005年的臭氧小时观测资料,首先对市区和郊区的大气臭氧浓度的污染特征和变化情况进行了分析,结果表明,上海市郊区的臭氧浓度和污染水平高于市区,且臭氧浓度日变化在市区呈现明显的双峰现象,而郊区则是单峰型变化规律。然后运用基于统计学理论的最小二乘支持向量机（LSSVM）方法对3个站的日（24h）平均和白日（12h）平均以及日最大值臭氧浓度进行预测,得到较好的预报效果。     

基于神经网络的城市夜间环境监控与预测

因夜间天空亮度分布具有非线性变化特点,故引入神经网络算法,建立基于时间序列的夜天空亮度预测模型,夜天空亮度预测模型可为城市光污染防治提供评价依据.文章对神经网络的原理进行了论述,建立了基于时间序列预测模型.以测试数据为训练样本集,基于MATLAB(矩阵实验室,Matrix Laboratory的简称),采用改进的BP算法(误差反向传播算法)对网络进行学习训练,并对存在的误差进行了分析.基于时间序列BP神经网络的夜天空预测模型,当隐含层神经元数目为5,训练函数为L-M优化算法(trainlm)时,最大绝对误差可达到0.003 6 cd/m2,最大相对误差达到2.361 4%.结果表明,模型的运行结果与试验数据比较吻合,输出与目标矢量之间相关性也较好.     

亚运时段广州大气污染物来源数值模拟研究

利用嵌套网格空气质量预报模式系统(Nested Air Quality Prediction Model System,NAQPMS)研究2006年亚运时段广州的空气质量状况,同时结合污染源追踪方法,分析珠三角各城市的源排放对广州全市、广州二环以内市区、广州6个亚运加强观测站的污染物浓度贡献.结果表明,NAQPMS模式能较好地反映广州各污染物(NO2、SO2、PM10)浓度的变化;广州全市、广州市区、6个亚运加强观测站的污染物最主要来源于本地排放,而周边城市以东莞的贡献最大.3个源受体中,广州市区受本地排放的影响最显著,来自本地的NO2、SO2、PM10的月均贡献率分别为89.5%、75.4%、86.7%;东莞则对6个亚运加强观测站的影响最为突出,其NO2、SO2、PM10的月均贡献率达9.3%、23.8%、21.7%,而日最大贡献率高达19.3%、40.2%、48.7%.因此在大力削减广州本地污染排放的同时,对周边城市特别是东莞实施区域联防联控,将能有效改善亚运场馆附近的空气质量.     

应用数值法对井工开采对地下水预测研究——以晋城矿区赵庄煤矿为例

为了预测井工煤矿开采后地下水水位变化,及对矿区内居民水源井的影响程度,文章在详细分析赵庄煤矿区域及井田水文地质条件的基础上,运用VisualMODFLOW软件对该煤矿及周边影响范围进行了地下水流数值模拟分析,预测了赵庄煤矿开采后不同阶段地下水水位、水量及影响范围的变化,并分析了煤矿开采对当地居民饮用水源的影响,模型的识别与检验表明,所建模型能够较好地反映水文地质条件,能够与目前实际开采地下水影响相吻合。